Some electric utilities have taken an interest in the detection of a plug-in electric vehicle's (PEV's) load using whole house meter interval data. For example, San Diego Gas & Electric has developed a patented heuristic algorithm to detect the presence of a PEV load that relies on four parameters to identify charging events: 1) a threshold level of total kWh consumption; 2) a defined duration at which kWh consumption remains above this threshold; 3) a leading edge increase in kWh consumption; and 4) a lagging edge decrease in kWh consumption. (Chen, et al., 2012)
The work of (Zhang, et al., 2011) directly applies a non-invasive load monitoring (NILM) technique to detect the presence of a charging PEV. This detection method employs pattern recognition by applying a normalized cross correlation of a specific load signature for a PEV and the whole house meter load. The charging load ramp up and ramp down unfortunately vary based on starting and ending battery state of charge, temperature, and vehicle manufacturer. Therefore, multiple signature patterns have to be tested for pattern recognition. Since the duration of load ramp up and ramp down are typically in the order of seconds to minutes, a high sampling rate is required to capture these features. In this example, the energy consumption is sampled every second (1 Hz).
Unfortunately for some smart meters deployed in residential installations, the energy consumption is sampled every hour (2.8E-4 Hz), and therefore load ramp up and ramp down features cannot be captured. As a result the detection methodology of (Zhang, et al., 2011) cannot be applied to these installations.
Several other attempts of load disaggregation and detection also require high sampling rates, prohibited by many production scale smart metering systems either through hardware or network bandwidth limitations. In (Weiss, Helfenstein, Mattern, & Thorsten, 2012), energy consumption is sampled every second (1 Hz) and both apparent and real power are measured. Apparent and real power are then run through a six step process that involves normalization, edge detection, power level computation, delta level computation, recognition, and labeling. In the implementation by (Du, et al., 2012), a high sampling rate of 30.72 kHz is used to measure energy consumption. This high sampling rate allows for conversion of the load into the spectral domain to capture detailed harmonic characteristics. A Support Vector Machine (SVM) from machine learning is then applied to detect the presence of different appliance loads. Unfortunately, SMUD's current AMI only measures real power and in hourly intervals, thereby inhibiting use of the algorithms proposed in (Weiss, Helfenstein, Mattern, & Thorsten, 2012) and (Du, et al., 2012).
Improvements are needed to existing methods for detecting the presence of plug-in electric vehicles based on low-frequency, such as hourly, whole house electric load metering data. The present invention provides a new and improved method to provide temporal segmentation/adaptation of the detection process by considering group or sub-group trends in whole house electric load metering data to enhance detection performance using existing detection methods.